The flexibility of models of recognition memory

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Despite decades of research, it is still a contested question which member of a set of competing models of recognition memory provides the most adequate account of the data. One of several factors contributing to this state of affairs is that the used model-selection measures only consider the number of parameters, but not the functional form of the models. We intend to develop measures of model flexibility that take functional form into account based on the minimum-description length principle. The resulting model selection measures will be useful for all research using these models in the future, not only in the field of recognition memory. In the present proposal, we focus on developing the model-selection measures for confidence rating data as collected by the vast majority of studies in the field. In a final step, the new methods are to be applied in a meta-analysis of existing data on recognition memory.